import cv2

import matplotlib.pyplot as plt

!pip install pytictoc

from pytictoc import TicToc

t = TicToc() #create instance of class

t.tic()

img = cv2.imread('me.jpg', 1)

img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

plt.imshow(img),plt.title('COLORIMAGE')#,plt.xticks([]),plt.yticks([])

plt.show()

t.toc()

plt.imshow(img),plt.title('COLORIMAGE'),plt.xticks([]),plt.yticks([])

plt.show()

t.tic()

img2 = cv2.imread('me.jpg', 1)

b,g,r = cv2.split(img2)

img2 =cv2.merge((r,g,b))

plt.imshow(img2),plt.title('COLORIMAGE'),plt.xticks([]),plt.yticks([])

plt.show()

t.toc()

j = 0

for filename in dir(cv2):

    if filename.startswith('COLOR_'):

        print(filename)

        j+=1

print('Ther are ' + str(j) + 'Colorspace Conversion flags in OpenCV')

import numpy as np

c = cv2.cvtColor(np.uint8([[[255,0,0]]]),cv2.COLOR_BGR2HSV)

print(c)

img3 = cv2.imread('cat.jpeg', 1)

img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2RGB)

plt.imshow(img3),plt.title('cat')#,plt.xticks([]),plt.yticks([])

plt.show()

a = np.array([40,50,50])

a

hsv = cv2.cvtColor(img3, cv2.COLOR_RGB2HSV)

image_mask = cv2.inRange(hsv, np.array([50,10,10]), np.array([255,255,255]))

​

output = cv2.bitwise_and(img3, img3, mask=image_mask)

plt.imshow(output),plt.title('output'),plt.xticks([]),plt.yticks([])

plt.show()

#blue = cv2.inRange(hsv, np.array([100,50,50]), np.array([255,255,255]))

brown = cv2.inRange(hsv, np.array([200,160,130]), np.array([255,255,255]))

orange = cv2.inRange(hsv, np.array([10,10,174]), np.array([110,255,255]))

​

#green = cv2.inRange(hsv, np.array([40,50,50]), np.array([255,255,255]))

image_mask = cv2.add(brown,orange)

output = cv2.bitwise_and(img3, img3, mask=image_mask)

plt.imshow(output),plt.title('output'),plt.xticks([]),plt.yticks([])

plt.show()

img4 = cv2.imread('bird.jpg', 1)

upscale = cv2.resize(img4, None, fx=1.5, fy=1.5, interpolation=cv2.INTER_CUBIC)

upscale = cv2.cvtColor(upscale,cv2.COLOR_BGR2RGB)

​

downscale = cv2.resize(img4, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA)

downscale = cv2.cvtColor(downscale,cv2.COLOR_BGR2RGB)

​

plt.figure(num='Image transformations',figsize=(12,8)) #1 inch = 0.39cm

plt.subplot(1,2,1)

plt.title('upscale')

plt.imshow(upscale)

​

plt.subplot(1,2,2)

plt.title('downscale')

plt.imshow(downscale)

plt.show()

j = 0

for filename in dir(cv2):

    if filename.startswith('INTER_'):

        print(filename)

        j+=1

print('Ther are ' + str(j) + 'interpolation method parameters in OpenCV')

img5 = cv2.imread('bird.jpg', 1)

input = cv2.cvtColor(img5, cv2.COLOR_BGR2RGB)

rows, cols, channel = input.shape

T = np.float32([[1,0,-50],[0,1,50]])

output = cv2.warpAffine(input, T, (cols,rows))

plt.imshow(output), plt.title('Shifted Image')

plt.show()

img6 = cv2.imread('bird.jpg', 1)

input = cv2.cvtColor(img6, cv2.COLOR_BGR2RGB)

rows, cols, channel = input.shape

R = cv2.getRotationMatrix2D((cols/2,rows/2),50,0.5)

output = cv2.warpAffine(input,R,(cols,rows))

plt.imshow(output), plt.title('Rotated and Downscaled Image')

plt.show()

from time import sleep

img7 = cv2.imread('bird.jpg', 1)

input = cv2.cvtColor(img7, cv2.COLOR_BGR2RGB)

rows, cols, channel = input.shape

​

angle = 0

while(1):

    if angle ==360:

        angle = 0

    M = cv2.getRotationMatrix2D((cols/2,rows/2),angle,1)

    rotated = cv2.warpAffine(input,M,(cols,rows))

    plt.imshow(rotated), plt.title('Rotating Image')

    plt.show()

    angle += 1

    sleep(0.2)

 import cv2

import numpy as np

from matplotlib import pyplot as plt

​

img9 = cv2.imread('rabbit.png', 1)

input = cv2.cvtColor(img9, cv2.COLOR_BGR2RGB)

rows, cols, channel = input.shape

points1 = np.float32([[100,100],[300,100],[100,300]])

points2 = np.float32([[200,150],[400,150],[100,300]])

​

A = cv2.getAffineTransform(points1,points2)

output = cv2.warpAffine(input,A,(cols,rows))

​

plt.figure(figsize=(12,8))

plt.subplot(121),plt.imshow(input),plt.title('Input')

plt.subplot(122),plt.imshow(output),plt.title('Affine Output')

plt.show()

import cv2

import numpy as np

from matplotlib import pyplot as plt

​

img10 = cv2.imread('friend.jpg', 1)

input = cv2.cvtColor(img10, cv2.COLOR_BGR2RGB)

rows, cols, channel = input.shape

points1 = np.float32([[0,0],[400,0],[0,400],[400,400]])

points2 = np.float32([[0,0],[300,0],[0,300],[300,300]])

​

P = cv2.getPerspectiveTransform(points1,points2)

output = cv2.warpPerspective(input,P,(200,300))

​

plt.figure(figsize=(12,8))

plt.subplot(121),plt.imshow(input),plt.title('Input')

plt.subplot(122),plt.imshow(output),plt.title('Perspective Transform')

plt.show()

import cv2

import numpy as np

from matplotlib import pyplot as plt

​

img11 = cv2.imread('friend.jpg', 1)

input = cv2.cvtColor(img11, cv2.COLOR_BGR2RGB)

rows, cols, channel = input.shape

points1 = np.float32([[0,0],[400,0],[0,400],[400,400]])

points2 = np.float32([[0,100],[300,0],[100,300],[300,300]])

​

P = cv2.getPerspectiveTransform(points1,points2)

output = cv2.warpPerspective(input,P,(300,300))

​

plt.figure(figsize=(12,8))

plt.subplot(121),plt.imshow(input),plt.title('Input')

plt.subplot(122),plt.imshow(output),plt.title('Perspective Transform')

plt.show()

j = 0

for filename in dir(cv2):

    if filename.startswith('THRESH'):

        print(filename)

        j+=1

print('Ther are ' + str(j) + 'threshold method parameters in OpenCV')

import cv2

from matplotlib import pyplot as plt

​

img12 = cv2.imread('grayscale.jpg', 0)

th = 127

max_val = 255

ret, o1 = cv2.threshold(img12,th,max_val,cv2.THRESH_BINARY)

ret, o2 = cv2.threshold(img12,th,max_val,cv2.THRESH_BINARY_INV)

ret, o3 = cv2.threshold(img12,th,max_val,cv2.THRESH_TOZERO)

ret, o4 = cv2.threshold(img12,th,max_val,cv2.THRESH_TOZERO_INV)

ret, o5 = cv2.threshold(img12,th,max_val,cv2.THRESH_TRUNC)

​

titles = ['Input Image','BINARY','BINARY_INV','TOZERO','TOZERO_INV','TRUNC']

output = [img12, o1, o2, o3, o4, o5]

for i in range(6):

  plt.subplot(2,3,i+1),plt.imshow(output[i],cmap='gray'),plt.title(titles[i]),plt.xticks([]),plt.yticks([])

plt.show()

​

import cv2

from matplotlib import pyplot as plt

​

img13 = cv2.imread('oni.png', 0)

​

ret, output = cv2.threshold(img13,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

​

plt.figure(figsize=(12,8))

plt.subplot(121),plt.imshow(img13,cmap='gray'),plt.title('Ibarakasen origin'),plt.xticks([]),plt.yticks([])

plt.subplot(122),plt.imshow(output,cmap='gray'),plt.title('Ibarakasen threshold'),plt.xticks([]),plt.yticks([])

plt.show()